Jennifer Mueller, Colorado State University
Authors: Jennifer Mueller, Talles Santos, Jari Kaipio, Raul Lima
2022 AWM Research Symposium
Recent Advancements in Inverse Problems and Imaging

Electrical impedance tomography (EIT) is a non-ionizing modality for real-time imaging of ventilation of patients with respiratory distress. Cross-sectional dynamic images are formed by reconstructing the conductivity distribution from measured voltage data arising from applied alternating currents on electrodes placed circumferentially around the chest. Since the conductivity of lung tissue depends on air content, blood flow, and the presence of pathology, the dynamic images provide regional information about ventilation, pulsatile perfusion, and abnormalities. However, due to the ill-posedness of the inverse conductivity problem, EIT images have coarse spatial resolution. One method of improving the resolution is to include prior information in the reconstruction. The D-bar method is a direct (non-iterative) reconstruction algorithm with a proven regularization strategy. Here we consider several techniques to include priors to post-process D-bar images to improve resolution. We will compare results from deterministic, statistical, and machine learning techniques applied to data from patients with cystic fibrosis at Children’s Hospital Colorado.

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